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The potential to narrow uncertainty in projections of regional precipitation change

Title
Publication TypeManual Entry
Year of Publication2011
AuthorsHawkins, E., and R. Sutton
Climate Dynamics
Volume37
Pagination407-418
Abstract

We separate and quantify the sources of uncertainty in projections of regional(similar to 2,500 km) precipitation changes for the twenty-first century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal mean precipitation, internal variability is the dominant uncertainty for predictions of the first decade everywhere, and for many regions until the third decade ahead. Model uncertainty is generally the dominant source of uncertainty for longer lead times. Scenario uncertainty is found to be small or negligible for all regions and lead times, apart from close to the poles at the end of the century. For the global mean, model uncertainty dominates at all lead times. The signal-to-noise ratio (S/N) of the precipitation projections is highest at the poles but less than 1 almost everywhere else, and is far lower than for temperature projections. In particular, the tropics have the highest S/N for temperature, but the lowest for precipitation. We also estimate a 'potential S/N' by assuming that model uncertainty could be reduced to zero, and show that, for regional precipitation, the gains in S/N are fairly modest, especially for predictions of the next few decades. This finding suggests that adaptation decisions will need to be made in the context of high uncertainty concerning regional changes in precipitation. The potential to narrow uncertainty in regional temperature projections is far greater. These conclusions on S/N are for the current generation of models; the real signal may be larger or smaller than the CMIP3 multi-model mean. Also note that the S/N for extreme precipitation, which is more relevant for many climate impacts, may be larger than for the seasonal mean precipitation considered here.

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Community Notes

This article gives a good description of projection uncertainty sources and their influence on projections throughout time.  They concluded that internal variability is the dominant source of uncertainty in the neart term.  Model uncertainty is the dominant source of uncertainty in the long term but this has potential for improvement as models are improved.  Scenario uncertainty is not considered significant except near the poles.  They also discuss the singal to noise ration which is used as a measure of robustness for a  projections.  The S/N ratio for precipitation is highest at the poles and is less than one in most other locations.  This ratio may be useful in my work for quantifying the uncertainty in future projections.  This article provides useful definitions and background information for my description of uncertainty.   

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